Literature DB >> 15120656

A computational TW3 classifier for skeletal maturity assessment. A Computing with Words approach.

Santiago Aja-Fernández1, Rodrigo De Luis-García, Miguel Angel Martín-Fernández, Carlos Alberola-López.   

Abstract

This paper proposes a fuzzy methodology to translate the natural language descriptions of the TW3 method for bone age assessment into an automatic classifier. The classifier is built upon a modified version of a fuzzy ID3 decision tree. No large data records are needed to train the classifier, i.e., to find out the classification rules, since the classifier is built upon rules given by the TW3 method. Only small data records are needed to fine-tune the fuzzy sets used to implement the rulebase.

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Year:  2004        PMID: 15120656     DOI: 10.1016/j.jbi.2004.01.002

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  5 in total

1.  Correlation and comparison of Risser sign versus bone age determination (TW3) between children with and without scoliosis in Korean population.

Authors:  Hitesh N Modi; Chetna H Modi; Seung Woo Suh; Jae-Hyuk Yang; Jae-Young Hong
Journal:  J Orthop Surg Res       Date:  2009-09-20       Impact factor: 2.359

2.  Probing an AI regression model for hand bone age determination using gradient-based saliency mapping.

Authors:  Zhiyue J Wang
Journal:  Sci Rep       Date:  2021-05-19       Impact factor: 4.379

3.  Automated bone age assessment: motivation, taxonomies, and challenges.

Authors:  Marjan Mansourvar; Maizatul Akmar Ismail; Tutut Herawan; Ram Gopal Raj; Sameem Abdul Kareem; Fariza Hanum Nasaruddin
Journal:  Comput Math Methods Med       Date:  2013-12-16       Impact factor: 2.238

4.  Clinical Validation of a Deep Learning-Based Hybrid (Greulich-Pyle and Modified Tanner-Whitehouse) Method for Bone Age Assessment.

Authors:  Kyu-Chong Lee; Kee-Hyoung Lee; Chang Ho Kang; Kyung-Sik Ahn; Lindsey Yoojin Chung; Jae-Joon Lee; Suk Joo Hong; Baek Hyun Kim; Euddeum Shim
Journal:  Korean J Radiol       Date:  2021-10-01       Impact factor: 3.500

5.  An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines.

Authors:  Marjan Mansourvar; Shahaboddin Shamshirband; Ram Gopal Raj; Roshan Gunalan; Iman Mazinani
Journal:  PLoS One       Date:  2015-09-24       Impact factor: 3.240

  5 in total

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